What type of neural networks are commonly used in AI for image recognition?

Convolutional neural networks (CNNs) are the preferred architecture for image recognition tasks due to their ability to effectively process data that has a grid-like topology, such as images. The design of CNNs allows them to automatically and adaptively learn spatial hierarchies of features from the input images.

One of the primary advantages of CNNs is their use of convolutional layers, which apply various filters to the input image to extract features such as edges, textures, and shapes. These filters assist in detecting patterns in localized regions of the image, making CNNs highly efficient compared to traditional neural networks that treat every pixel independently. This is especially important in image recognition, where patterns often exist at multiple scales and positions within an image.

Moreover, CNNs typically employ pooling layers that reduce the dimensionality of the feature maps, which helps in making the model more computationally efficient while retaining the most important features. The hierarchical structure of CNNs—combining lower-level features into more complex representations—enables them to achieve high levels of accuracy in image classification tasks.

In contrast, other types of networks like recurrent neural networks (RNNs) are better suited for sequential data like time series or natural language processing, as they excel in capturing temporal dependencies.